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I started learning ridge regression in R. I applied the linear ridge regression to my full data set and got the following results.

gridge<-lm.ridge(divorce ~., data=divusa, lambda=seq(0,35,0.02)) 
select(gridge) 
modified HKB estimator is 0.07693804 
modified L-W estimator is 0.3088377 
smallest value of GCV at 0.02 which.min(gridge$GCV) 
0.02 
2 

round(coef(gridge)[2,-1],3) 
year   unemployed femlab marriage birth military
-0.195  -0.053    0.790    0.148 -0.118   -0.042      

round(coef(g)[-1],3)
 year unemployed femlab marriage birth  military
-0.203  -0.049   0.808    0.150 -0.117 -0.043 

Questions:

  1. How do I interpret the results?
  2. Do I have to do anything else for interpretation?
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Some things to look at when fitting the ridge regression

regression coefficients for this fit:

round(gridge$coef[, which(gridge$lambda ==.02)], 2)

ordinary least square fit:

round(gridge$coef[, which(gridge$lambda == 0)], 2)

The ridge regression centers and scales the predictors so you need to do the same when calculating the fit. You can add back the mean of the response.

more info on ridge regression: http://tamino.wordpress.com/2011/02/12/ridge-regression/

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  • $\begingroup$ Hello. What R package do you advise to use for Ridge Regression? glmnet, bigRR, Mass, other? Any of them able to deal with repeated measures (random effects)? $\endgroup$ – skan Jun 4 '16 at 18:49

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